Benthic river algae mapping using hyperspectral imagery from unoccupied aerial vehicles

Loading...
Thumbnail Image

Date

2024-06

Journal Title

Journal ISSN

Volume Title

Publisher

SPIE-Intl Soc Optical Eng

Abstract

The increasing prevalence of nuisance benthic algal blooms in freshwater systems has led to water quality monitoring programs based on the presence and abundance of algae. Large blooms of the nuisance filamentous algae, Cladophora glomerata, have become common in the waters of the Upper Clark Fork River in western Montana. To aid in the understanding of algal growth dynamics, unoccupied aerial vehicle (UAV)-based hyperspectral images were gathered at three field sites along the length of the river throughout the growing season of 2021. Select regions within images covering the spectral range of 400 to 850 nm were labeled based on a combination of professional judgment and spectral profiles and used to train a random forest classifier to identify benthic algal growth across several classes, including benthic growth dominated by Cladophora (Clado), benthic growth dominated by growth forms other than Cladophora (non-Clado), and areas below a visually detectable threshold of benthic growth (bare substrate). After classification, images were stitched together to produce spatial distribution maps of each river reach while also calculating the average percent cover for each reach, achieving an accuracy of approximately 99% relative to manually labeled images. Results of this analysis showed strong variability across each reach, both temporally (up to 40%) and spatially (up to 46%), indicating that UAV-based imaging with high-spatial resolution could augment and therefore improve traditional measurement techniques that are spatially limited, such as spot sampling.

Description

Keywords

river algae, hyperspectral imaging, unoccupied aerial vehicles, water optics, water quality

Citation

Riley D. Logan and Joseph A. Shaw "Benthic river algae mapping using hyperspectral imagery from unoccupied aerial vehicles," Journal of Applied Remote Sensing 18(2), 024513 (13 June 2024). https://doi.org/10.1117/1.JRS.18.024513
Copyright (c) 2002-2022, LYRASIS. All rights reserved.